Overview

Dataset statistics

Number of variables31
Number of observations61222
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.5 MiB
Average record size in memory248.0 B

Variable types

Numeric21
Categorical10

Alerts

Accounts Delinquent has constant value "0"Constant
Loan Status has constant value "0"Constant
Public Record is highly imbalanced (82.9%)Imbalance
Collection 12 months Medical is highly imbalanced (85.1%)Imbalance
Application Type is highly imbalanced (98.1%)Imbalance
ID has unique valuesUnique
Delinquency - two years has 47277 (77.2%) zerosZeros
Inquires - six months has 54887 (89.7%) zerosZeros

Reproduction

Analysis started2024-05-21 16:58:21.573490
Analysis finished2024-05-21 16:58:46.385560
Duration24.81 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIQUE 

Distinct61222
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25624431
Minimum1298156
Maximum72245779
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:46.425488image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1298156
5-th percentile2289741.6
Q16566992.8
median17900004
Q342702170
95-th percentile63745001
Maximum72245779
Range70947623
Interquartile range (IQR)36135177

Descriptive statistics

Standard deviation21090401
Coefficient of variation (CV)0.82305832
Kurtosis-1.0879127
Mean25624431
Median Absolute Deviation (MAD)14282920
Skewness0.55792427
Sum1.5687789 × 1012
Variance4.4480501 × 1014
MonotonicityNot monotonic
2024-05-21T11:58:46.482263image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65087372 1
 
< 0.1%
64224763 1
 
< 0.1%
69112031 1
 
< 0.1%
7009586 1
 
< 0.1%
22088220 1
 
< 0.1%
10867815 1
 
< 0.1%
8607102 1
 
< 0.1%
7945397 1
 
< 0.1%
67169611 1
 
< 0.1%
10375342 1
 
< 0.1%
Other values (61212) 61212
> 99.9%
ValueCountFrequency (%)
1298156 1
< 0.1%
1298576 1
< 0.1%
1298988 1
< 0.1%
1299125 1
< 0.1%
1299130 1
< 0.1%
1300238 1
< 0.1%
1300577 1
< 0.1%
1300838 1
< 0.1%
1301081 1
< 0.1%
1301420 1
< 0.1%
ValueCountFrequency (%)
72245779 1
< 0.1%
72191501 1
< 0.1%
72187231 1
< 0.1%
72182515 1
< 0.1%
72134965 1
< 0.1%
72113342 1
< 0.1%
72101854 1
< 0.1%
72076506 1
< 0.1%
72065755 1
< 0.1%
72050446 1
< 0.1%

Loan Amount
Real number (ℝ)

Distinct26624
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16860.853
Minimum1014
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:46.537083image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1014
5-th percentile4503
Q110042.25
median16087
Q322098
95-th percentile31736.95
Maximum35000
Range33986
Interquartile range (IQR)12055.75

Descriptive statistics

Standard deviation8357.3814
Coefficient of variation (CV)0.49566777
Kurtosis-0.79555971
Mean16860.853
Median Absolute Deviation (MAD)6031
Skewness0.28870263
Sum1.0322551 × 109
Variance69845823
MonotonicityNot monotonic
2024-05-21T11:58:46.594360image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15118 11
 
< 0.1%
14424 11
 
< 0.1%
14556 11
 
< 0.1%
15962 11
 
< 0.1%
16216 10
 
< 0.1%
15564 10
 
< 0.1%
15932 10
 
< 0.1%
15639 10
 
< 0.1%
14689 10
 
< 0.1%
15165 10
 
< 0.1%
Other values (26614) 61118
99.8%
ValueCountFrequency (%)
1014 1
< 0.1%
1020 1
< 0.1%
1024 1
< 0.1%
1025 1
< 0.1%
1030 1
< 0.1%
1031 1
< 0.1%
1036 1
< 0.1%
1045 1
< 0.1%
1046 1
< 0.1%
1048 1
< 0.1%
ValueCountFrequency (%)
35000 1
< 0.1%
34999 1
< 0.1%
34996 1
< 0.1%
34995 1
< 0.1%
34993 1
< 0.1%
34991 1
< 0.1%
34990 1
< 0.1%
34988 2
< 0.1%
34987 1
< 0.1%
34986 1
< 0.1%

Funded Amount
Real number (ℝ)

Distinct23748
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15767.049
Minimum1014
Maximum34999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:46.650098image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1014
5-th percentile5903
Q19255
median13032
Q321801
95-th percentile32121
Maximum34999
Range33985
Interquartile range (IQR)12546

Descriptive statistics

Standard deviation8154.6828
Coefficient of variation (CV)0.51719778
Kurtosis-0.61667316
Mean15767.049
Median Absolute Deviation (MAD)5086
Skewness0.67344113
Sum9.6529028 × 108
Variance66498852
MonotonicityNot monotonic
2024-05-21T11:58:46.707485image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11034 15
 
< 0.1%
10835 15
 
< 0.1%
9011 13
 
< 0.1%
8860 13
 
< 0.1%
10728 13
 
< 0.1%
11187 13
 
< 0.1%
11451 13
 
< 0.1%
7691 12
 
< 0.1%
10860 12
 
< 0.1%
11197 12
 
< 0.1%
Other values (23738) 61091
99.8%
ValueCountFrequency (%)
1014 1
< 0.1%
1032 1
< 0.1%
1080 1
< 0.1%
1087 1
< 0.1%
1098 1
< 0.1%
1154 1
< 0.1%
1163 1
< 0.1%
1179 1
< 0.1%
1236 1
< 0.1%
1249 1
< 0.1%
ValueCountFrequency (%)
34999 2
< 0.1%
34998 2
< 0.1%
34995 1
< 0.1%
34994 1
< 0.1%
34993 1
< 0.1%
34988 1
< 0.1%
34986 2
< 0.1%
34983 1
< 0.1%
34982 2
< 0.1%
34977 1
< 0.1%

Funded Amount Investor
Real number (ℝ)

Distinct61205
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14621.996
Minimum1114.5902
Maximum34999.746
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:46.761449image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1114.5902
5-th percentile6334.9196
Q19845.6848
median12806.409
Q317748.224
95-th percentile28884.343
Maximum34999.746
Range33885.156
Interquartile range (IQR)7902.5393

Descriptive statistics

Standard deviation6776.6712
Coefficient of variation (CV)0.46345731
Kurtosis0.47385214
Mean14621.996
Median Absolute Deviation (MAD)3557.2668
Skewness0.99305992
Sum8.9518786 × 108
Variance45923272
MonotonicityNot monotonic
2024-05-21T11:58:46.820893image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10709.63623 2
 
< 0.1%
7890.447955 2
 
< 0.1%
13971.02321 2
 
< 0.1%
12231.16637 2
 
< 0.1%
14240.83015 2
 
< 0.1%
9520.450589 2
 
< 0.1%
14043.45039 2
 
< 0.1%
12367.56806 2
 
< 0.1%
10936.53653 2
 
< 0.1%
13692.84975 2
 
< 0.1%
Other values (61195) 61202
> 99.9%
ValueCountFrequency (%)
1114.590204 1
< 0.1%
1127.754818 1
< 0.1%
1129.708853 1
< 0.1%
1242.527961 1
< 0.1%
1246.547591 1
< 0.1%
1250.787941 1
< 0.1%
1372.686804 1
< 0.1%
1441.583282 1
< 0.1%
1525.567016 1
< 0.1%
1537.528946 1
< 0.1%
ValueCountFrequency (%)
34999.74643 1
< 0.1%
34999.43383 1
< 0.1%
34997.89175 1
< 0.1%
34996.88747 1
< 0.1%
34995.26246 1
< 0.1%
34993.60145 1
< 0.1%
34993.49979 1
< 0.1%
34990.5952 1
< 0.1%
34988.98401 1
< 0.1%
34987.513 1
< 0.1%

Term
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size478.4 KiB
59
39529 
58
20390 
36
 
1303

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters122444
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row59
2nd row59
3rd row59
4th row59
5th row59

Common Values

ValueCountFrequency (%)
59 39529
64.6%
58 20390
33.3%
36 1303
 
2.1%

Length

2024-05-21T11:58:46.874339image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T11:58:46.917761image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
59 39529
64.6%
58 20390
33.3%
36 1303
 
2.1%

Most occurring characters

ValueCountFrequency (%)
5 59919
48.9%
9 39529
32.3%
8 20390
 
16.7%
3 1303
 
1.1%
6 1303
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 59919
48.9%
9 39529
32.3%
8 20390
 
16.7%
3 1303
 
1.1%
6 1303
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 59919
48.9%
9 39529
32.3%
8 20390
 
16.7%
3 1303
 
1.1%
6 1303
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 59919
48.9%
9 39529
32.3%
8 20390
 
16.7%
3 1303
 
1.1%
6 1303
 
1.1%

Interest Rate
Real number (ℝ)

Distinct61210
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.842815
Minimum5.3200058
Maximum27.182348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:46.967736image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum5.3200058
5-th percentile6.1336266
Q19.2985479
median11.376376
Q314.189447
95-th percentile18.571065
Maximum27.182348
Range21.862342
Interquartile range (IQR)4.8908992

Descriptive statistics

Standard deviation3.7113777
Coefficient of variation (CV)0.31338645
Kurtosis0.13701876
Mean11.842815
Median Absolute Deviation (MAD)2.3777365
Skewness0.55776919
Sum725040.82
Variance13.774325
MonotonicityNot monotonic
2024-05-21T11:58:47.023582image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.97529763 2
 
< 0.1%
15.78960848 2
 
< 0.1%
10.15106863 2
 
< 0.1%
9.28661329 2
 
< 0.1%
8.155726081 2
 
< 0.1%
9.912497484 2
 
< 0.1%
8.637499241 2
 
< 0.1%
9.031164731 2
 
< 0.1%
11.97836961 2
 
< 0.1%
6.191126068 2
 
< 0.1%
Other values (61200) 61202
> 99.9%
ValueCountFrequency (%)
5.320005799 1
< 0.1%
5.320159165 1
< 0.1%
5.320433439 1
< 0.1%
5.320547017 1
< 0.1%
5.321130759 1
< 0.1%
5.321256189 1
< 0.1%
5.322212834 1
< 0.1%
5.322458098 1
< 0.1%
5.322651425 1
< 0.1%
5.322937103 1
< 0.1%
ValueCountFrequency (%)
27.18234758 1
< 0.1%
27.07000405 1
< 0.1%
27.01820329 1
< 0.1%
26.9329474 1
< 0.1%
26.92044891 1
< 0.1%
26.83306079 1
< 0.1%
26.54588757 1
< 0.1%
26.51588192 1
< 0.1%
26.32641724 1
< 0.1%
26.31597117 1
< 0.1%

Grade
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size478.4 KiB
C
17293 
B
17107 
A
10955 
D
7463 
E
5828 
Other values (2)
2576 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters61222
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowF
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 17293
28.2%
B 17107
27.9%
A 10955
17.9%
D 7463
12.2%
E 5828
 
9.5%
F 2013
 
3.3%
G 563
 
0.9%

Length

2024-05-21T11:58:47.072908image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T11:58:47.117786image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
c 17293
28.2%
b 17107
27.9%
a 10955
17.9%
d 7463
12.2%
e 5828
 
9.5%
f 2013
 
3.3%
g 563
 
0.9%

Most occurring characters

ValueCountFrequency (%)
C 17293
28.2%
B 17107
27.9%
A 10955
17.9%
D 7463
12.2%
E 5828
 
9.5%
F 2013
 
3.3%
G 563
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 17293
28.2%
B 17107
27.9%
A 10955
17.9%
D 7463
12.2%
E 5828
 
9.5%
F 2013
 
3.3%
G 563
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 17293
28.2%
B 17107
27.9%
A 10955
17.9%
D 7463
12.2%
E 5828
 
9.5%
F 2013
 
3.3%
G 563
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 17293
28.2%
B 17107
27.9%
A 10955
17.9%
D 7463
12.2%
E 5828
 
9.5%
F 2013
 
3.3%
G 563
 
0.9%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size478.4 KiB
MORTGAGE
33128 
RENT
21839 
OWN
6255 

Length

Max length8
Median length8
Mean length6.0622815
Min length3

Characters and Unicode

Total characters371145
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMORTGAGE
2nd rowRENT
3rd rowMORTGAGE
4th rowMORTGAGE
5th rowMORTGAGE

Common Values

ValueCountFrequency (%)
MORTGAGE 33128
54.1%
RENT 21839
35.7%
OWN 6255
 
10.2%

Length

2024-05-21T11:58:47.169642image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T11:58:47.209985image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
mortgage 33128
54.1%
rent 21839
35.7%
own 6255
 
10.2%

Most occurring characters

ValueCountFrequency (%)
G 66256
17.9%
R 54967
14.8%
T 54967
14.8%
E 54967
14.8%
O 39383
10.6%
M 33128
8.9%
A 33128
8.9%
N 28094
7.6%
W 6255
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 371145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 66256
17.9%
R 54967
14.8%
T 54967
14.8%
E 54967
14.8%
O 39383
10.6%
M 33128
8.9%
A 33128
8.9%
N 28094
7.6%
W 6255
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 371145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 66256
17.9%
R 54967
14.8%
T 54967
14.8%
E 54967
14.8%
O 39383
10.6%
M 33128
8.9%
A 33128
8.9%
N 28094
7.6%
W 6255
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 371145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 66256
17.9%
R 54967
14.8%
T 54967
14.8%
E 54967
14.8%
O 39383
10.6%
M 33128
8.9%
A 33128
8.9%
N 28094
7.6%
W 6255
 
1.7%

Home Ownership
Real number (ℝ)

Distinct61215
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80488.081
Minimum14573.537
Maximum406561.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:47.259681image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum14573.537
5-th percentile33522.487
Q151678.569
median69274.74
Q394458.039
95-th percentile168095.45
Maximum406561.54
Range391988
Interquartile range (IQR)42779.469

Descriptive statistics

Standard deviation44982.239
Coefficient of variation (CV)0.55886833
Kurtosis7.0789422
Mean80488.081
Median Absolute Deviation (MAD)20056.556
Skewness2.1377581
Sum4.9276413 × 109
Variance2.0234019 × 109
MonotonicityNot monotonic
2024-05-21T11:58:47.319583image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37623.24185 2
 
< 0.1%
27139.67231 2
 
< 0.1%
58867.57595 2
 
< 0.1%
71159.7124 2
 
< 0.1%
61831.12988 2
 
< 0.1%
35858.04083 2
 
< 0.1%
39753.81982 2
 
< 0.1%
155158.436 1
 
< 0.1%
167523.331 1
 
< 0.1%
50944.31929 1
 
< 0.1%
Other values (61205) 61205
> 99.9%
ValueCountFrequency (%)
14573.53717 1
< 0.1%
14652.37968 1
< 0.1%
14678.63863 1
< 0.1%
14788.61394 1
< 0.1%
14836.55226 1
< 0.1%
14859.64954 1
< 0.1%
14901.41773 1
< 0.1%
14938.0786 1
< 0.1%
14996.99281 1
< 0.1%
15013.52595 1
< 0.1%
ValueCountFrequency (%)
406561.5364 1
< 0.1%
405697.0616 1
< 0.1%
404550.444 1
< 0.1%
401352.3764 1
< 0.1%
400877.5635 1
< 0.1%
400676.3457 1
< 0.1%
399925.7864 1
< 0.1%
399103.7444 1
< 0.1%
398416.3107 1
< 0.1%
397669.9965 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size478.4 KiB
Source Verified
29951 
Verified
16428 
Not Verified
14843 

Length

Max length15
Median length12
Mean length12.394319
Min length8

Characters and Unicode

Total characters758805
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Verified
2nd rowSource Verified
3rd rowSource Verified
4th rowSource Verified
5th rowSource Verified

Common Values

ValueCountFrequency (%)
Source Verified 29951
48.9%
Verified 16428
26.8%
Not Verified 14843
24.2%

Length

2024-05-21T11:58:47.376887image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T11:58:47.421410image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
verified 61222
57.7%
source 29951
28.3%
not 14843
 
14.0%

Most occurring characters

ValueCountFrequency (%)
e 152395
20.1%
i 122444
16.1%
r 91173
12.0%
V 61222
8.1%
f 61222
8.1%
d 61222
8.1%
o 44794
 
5.9%
44794
 
5.9%
S 29951
 
3.9%
u 29951
 
3.9%
Other values (3) 59637
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 758805
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 152395
20.1%
i 122444
16.1%
r 91173
12.0%
V 61222
8.1%
f 61222
8.1%
d 61222
8.1%
o 44794
 
5.9%
44794
 
5.9%
S 29951
 
3.9%
u 29951
 
3.9%
Other values (3) 59637
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 758805
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 152395
20.1%
i 122444
16.1%
r 91173
12.0%
V 61222
8.1%
f 61222
8.1%
d 61222
8.1%
o 44794
 
5.9%
44794
 
5.9%
S 29951
 
3.9%
u 29951
 
3.9%
Other values (3) 59637
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 758805
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 152395
20.1%
i 122444
16.1%
r 91173
12.0%
V 61222
8.1%
f 61222
8.1%
d 61222
8.1%
o 44794
 
5.9%
44794
 
5.9%
S 29951
 
3.9%
u 29951
 
3.9%
Other values (3) 59637
 
7.9%

Debit to Income
Real number (ℝ)

Distinct61216
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.307491
Minimum0.67529909
Maximum39.629862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:47.470842image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.67529909
5-th percentile10.20328
Q116.760928
median22.652777
Q330.069674
95-th percentile37.404569
Maximum39.629862
Range38.954563
Interquartile range (IQR)13.308746

Descriptive statistics

Standard deviation8.4589768
Coefficient of variation (CV)0.36292953
Kurtosis-0.90882998
Mean23.307491
Median Absolute Deviation (MAD)6.5511812
Skewness0.081079979
Sum1426931.2
Variance71.554288
MonotonicityNot monotonic
2024-05-21T11:58:47.528785image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.97736061 2
 
< 0.1%
22.36852703 2
 
< 0.1%
27.34419347 2
 
< 0.1%
18.79251904 2
 
< 0.1%
17.62506899 2
 
< 0.1%
24.41063595 2
 
< 0.1%
20.32497351 1
 
< 0.1%
12.2347598 1
 
< 0.1%
17.58717481 1
 
< 0.1%
16.28475781 1
 
< 0.1%
Other values (61206) 61206
> 99.9%
ValueCountFrequency (%)
0.675299086 1
< 0.1%
0.763630198 1
< 0.1%
1.300557774 1
< 0.1%
1.329297599 1
< 0.1%
1.372873946 1
< 0.1%
1.391419093 1
< 0.1%
1.397374381 1
< 0.1%
1.425369584 1
< 0.1%
1.757425556 1
< 0.1%
1.803413281 1
< 0.1%
ValueCountFrequency (%)
39.62986189 1
< 0.1%
39.62964383 1
< 0.1%
39.6278639 1
< 0.1%
39.62757603 1
< 0.1%
39.62741588 1
< 0.1%
39.62731 1
< 0.1%
39.62664966 1
< 0.1%
39.62590781 1
< 0.1%
39.62567144 1
< 0.1%
39.624746 1
< 0.1%

Delinquency - two years
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32457287
Minimum0
Maximum8
Zeros47277
Zeros (%)77.2%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:47.575130image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.79319203
Coefficient of variation (CV)2.4438027
Kurtosis30.87995
Mean0.32457287
Median Absolute Deviation (MAD)0
Skewness4.6329703
Sum19871
Variance0.6291536
MonotonicityNot monotonic
2024-05-21T11:58:47.616677image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 47277
77.2%
1 10644
 
17.4%
2 2396
 
3.9%
3 401
 
0.7%
7 225
 
0.4%
6 164
 
0.3%
5 65
 
0.1%
8 37
 
0.1%
4 13
 
< 0.1%
ValueCountFrequency (%)
0 47277
77.2%
1 10644
 
17.4%
2 2396
 
3.9%
3 401
 
0.7%
4 13
 
< 0.1%
5 65
 
0.1%
6 164
 
0.3%
7 225
 
0.4%
8 37
 
0.1%
ValueCountFrequency (%)
8 37
 
0.1%
7 225
 
0.4%
6 164
 
0.3%
5 65
 
0.1%
4 13
 
< 0.1%
3 401
 
0.7%
2 2396
 
3.9%
1 10644
 
17.4%
0 47277
77.2%

Inquires - six months
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14566659
Minimum0
Maximum5
Zeros54887
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:47.661212image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.47282786
Coefficient of variation (CV)3.2459595
Kurtosis15.1331
Mean0.14566659
Median Absolute Deviation (MAD)0
Skewness3.7104392
Sum8918
Variance0.22356618
MonotonicityNot monotonic
2024-05-21T11:58:47.700052image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 54887
89.7%
1 4147
 
6.8%
2 1846
 
3.0%
3 292
 
0.5%
4 47
 
0.1%
5 3
 
< 0.1%
ValueCountFrequency (%)
0 54887
89.7%
1 4147
 
6.8%
2 1846
 
3.0%
3 292
 
0.5%
4 47
 
0.1%
5 3
 
< 0.1%
ValueCountFrequency (%)
5 3
 
< 0.1%
4 47
 
0.1%
3 292
 
0.5%
2 1846
 
3.0%
1 4147
 
6.8%
0 54887
89.7%

Open Account
Real number (ℝ)

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.280618
Minimum2
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:47.758118image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q110
median13
Q316
95-th percentile29
Maximum37
Range35
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.2260481
Coefficient of variation (CV)0.4359789
Kurtosis1.8255254
Mean14.280618
Median Absolute Deviation (MAD)3
Skewness1.4666612
Sum874288
Variance38.763676
MonotonicityNot monotonic
2024-05-21T11:58:47.804888image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
12 7686
12.6%
13 7229
11.8%
11 6632
10.8%
14 5501
 
9.0%
10 5260
 
8.6%
9 4201
 
6.9%
15 3048
 
5.0%
8 2823
 
4.6%
16 1918
 
3.1%
7 1707
 
2.8%
Other values (26) 15217
24.9%
ValueCountFrequency (%)
2 5
 
< 0.1%
3 38
 
0.1%
4 172
 
0.3%
5 422
 
0.7%
6 924
 
1.5%
7 1707
 
2.8%
8 2823
4.6%
9 4201
6.9%
10 5260
8.6%
11 6632
10.8%
ValueCountFrequency (%)
37 87
 
0.1%
36 140
 
0.2%
35 210
 
0.3%
34 322
0.5%
33 457
0.7%
32 463
0.8%
31 501
0.8%
30 584
1.0%
29 527
0.9%
28 618
1.0%

Public Record
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size478.4 KiB
0
57098 
1
 
3719
2
 
178
4
 
158
3
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters61222
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 57098
93.3%
1 3719
 
6.1%
2 178
 
0.3%
4 158
 
0.3%
3 69
 
0.1%

Length

2024-05-21T11:58:47.865659image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T11:58:47.904795image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 57098
93.3%
1 3719
 
6.1%
2 178
 
0.3%
4 158
 
0.3%
3 69
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 57098
93.3%
1 3719
 
6.1%
2 178
 
0.3%
4 158
 
0.3%
3 69
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 57098
93.3%
1 3719
 
6.1%
2 178
 
0.3%
4 158
 
0.3%
3 69
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 57098
93.3%
1 3719
 
6.1%
2 178
 
0.3%
4 158
 
0.3%
3 69
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 57098
93.3%
1 3719
 
6.1%
2 178
 
0.3%
4 158
 
0.3%
3 69
 
0.1%

Revolving Balance
Real number (ℝ)

Distinct19904
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7702.0276
Minimum0
Maximum114621
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:47.956899image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile528
Q12548
median5508
Q310206
95-th percentile22450.95
Maximum114621
Range114621
Interquartile range (IQR)7658

Descriptive statistics

Standard deviation7847.4061
Coefficient of variation (CV)1.0188754
Kurtosis16.682589
Mean7702.0276
Median Absolute Deviation (MAD)3478
Skewness2.9415062
Sum4.7153353 × 108
Variance61581782
MonotonicityNot monotonic
2024-05-21T11:58:48.012850image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1394 17
 
< 0.1%
829 15
 
< 0.1%
3997 15
 
< 0.1%
869 15
 
< 0.1%
1252 14
 
< 0.1%
1525 14
 
< 0.1%
80 14
 
< 0.1%
574 14
 
< 0.1%
2363 14
 
< 0.1%
1202 14
 
< 0.1%
Other values (19894) 61076
99.8%
ValueCountFrequency (%)
0 7
< 0.1%
1 10
< 0.1%
2 5
< 0.1%
3 6
< 0.1%
4 9
< 0.1%
5 6
< 0.1%
6 6
< 0.1%
7 6
< 0.1%
8 6
< 0.1%
9 6
< 0.1%
ValueCountFrequency (%)
114621 1
< 0.1%
111223 1
< 0.1%
108050 1
< 0.1%
105820 1
< 0.1%
104159 1
< 0.1%
104133 1
< 0.1%
103901 1
< 0.1%
99484 1
< 0.1%
97895 1
< 0.1%
95226 1
< 0.1%

Revolving Utilities
Real number (ℝ)

Distinct61218
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.859795
Minimum0.00517236
Maximum100.88005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:48.072736image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.00517236
5-th percentile11.588391
Q138.623246
median54.051977
Q369.146929
95-th percentile88.49271
Maximum100.88005
Range100.87488
Interquartile range (IQR)30.523683

Descriptive statistics

Standard deviation22.542163
Coefficient of variation (CV)0.42645196
Kurtosis-0.54277752
Mean52.859795
Median Absolute Deviation (MAD)15.246958
Skewness-0.23629977
Sum3236182.3
Variance508.1491
MonotonicityNot monotonic
2024-05-21T11:58:48.131373image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.55837053 2
 
< 0.1%
91.31748448 2
 
< 0.1%
39.61185859 2
 
< 0.1%
9.409246945 2
 
< 0.1%
74.93255103 1
 
< 0.1%
59.42249964 1
 
< 0.1%
51.29358823 1
 
< 0.1%
36.10440459 1
 
< 0.1%
52.86014983 1
 
< 0.1%
64.46773871 1
 
< 0.1%
Other values (61208) 61208
> 99.9%
ValueCountFrequency (%)
0.00517236 1
< 0.1%
0.021283828 1
< 0.1%
0.02999706 1
< 0.1%
0.035816294 1
< 0.1%
0.044888253 1
< 0.1%
0.051037999 1
< 0.1%
0.051458193 1
< 0.1%
0.054950741 1
< 0.1%
0.058213542 1
< 0.1%
0.05911439 1
< 0.1%
ValueCountFrequency (%)
100.8800498 1
< 0.1%
100.8668139 1
< 0.1%
100.8586132 1
< 0.1%
100.8549743 1
< 0.1%
100.8356926 1
< 0.1%
100.8355207 1
< 0.1%
100.8265117 1
< 0.1%
100.8231983 1
< 0.1%
100.798556 1
< 0.1%
100.7822737 1
< 0.1%

Total Accounts
Real number (ℝ)

Distinct68
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.62734
Minimum4
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:48.185405image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q113
median18
Q323
95-th percentile33
Maximum72
Range68
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.2939759
Coefficient of variation (CV)0.4452582
Kurtosis1.3509324
Mean18.62734
Median Absolute Deviation (MAD)5
Skewness0.73794298
Sum1140403
Variance68.790035
MonotonicityNot monotonic
2024-05-21T11:58:48.253977image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 3433
 
5.6%
17 3390
 
5.5%
19 3372
 
5.5%
20 3237
 
5.3%
16 3111
 
5.1%
21 3039
 
5.0%
22 2734
 
4.5%
15 2733
 
4.5%
23 2389
 
3.9%
14 2326
 
3.8%
Other values (58) 31458
51.4%
ValueCountFrequency (%)
4 1017
1.7%
5 1185
1.9%
6 1280
2.1%
7 1604
2.6%
8 1783
2.9%
9 1877
3.1%
10 2026
3.3%
11 1949
3.2%
12 1921
3.1%
13 2042
3.3%
ValueCountFrequency (%)
72 2
 
< 0.1%
71 2
 
< 0.1%
70 1
 
< 0.1%
68 3
< 0.1%
67 2
 
< 0.1%
66 1
 
< 0.1%
65 7
< 0.1%
64 3
< 0.1%
63 3
< 0.1%
62 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size478.4 KiB
w
33072 
f
28150 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters61222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roww
2nd rowf
3rd roww
4th roww
5th roww

Common Values

ValueCountFrequency (%)
w 33072
54.0%
f 28150
46.0%

Length

2024-05-21T11:58:48.307453image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T11:58:48.346215image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
w 33072
54.0%
f 28150
46.0%

Most occurring characters

ValueCountFrequency (%)
w 33072
54.0%
f 28150
46.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w 33072
54.0%
f 28150
46.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w 33072
54.0%
f 28150
46.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w 33072
54.0%
f 28150
46.0%

Total Received Interest
Real number (ℝ)

Distinct61212
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2067.801
Minimum4.7367463
Maximum14301.368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:48.389910image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum4.7367463
5-th percentile159.62026
Q1569.21405
median1329.8068
Q32658.9019
95-th percentile6911.6308
Maximum14301.368
Range14296.632
Interquartile range (IQR)2089.6878

Descriptive statistics

Standard deviation2218.6353
Coefficient of variation (CV)1.0729443
Kurtosis5.1505996
Mean2067.801
Median Absolute Deviation (MAD)905.98819
Skewness2.1283809
Sum1.2659491 × 108
Variance4922342.7
MonotonicityNot monotonic
2024-05-21T11:58:48.443811image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
672.3328503 2
 
< 0.1%
3174.594809 2
 
< 0.1%
437.9250212 2
 
< 0.1%
941.274347 2
 
< 0.1%
453.6216698 2
 
< 0.1%
525.9471228 2
 
< 0.1%
1530.587463 2
 
< 0.1%
9061.050032 2
 
< 0.1%
658.0427657 2
 
< 0.1%
654.1538512 2
 
< 0.1%
Other values (61202) 61202
> 99.9%
ValueCountFrequency (%)
4.736746327 1
< 0.1%
4.740085405 1
< 0.1%
5.029317935 1
< 0.1%
5.037685745 1
< 0.1%
5.121524759 1
< 0.1%
5.167160376 1
< 0.1%
5.307849325 1
< 0.1%
5.397308646 1
< 0.1%
5.486680943 1
< 0.1%
5.535147996 1
< 0.1%
ValueCountFrequency (%)
14301.36831 1
< 0.1%
14290.59148 1
< 0.1%
14281.46799 1
< 0.1%
14256.74704 1
< 0.1%
14255.15602 1
< 0.1%
14236.69012 1
< 0.1%
14227.81424 1
< 0.1%
14195.13756 1
< 0.1%
14172.13655 1
< 0.1%
14170.63244 1
< 0.1%

Total Received Late Fee
Real number (ℝ)

Distinct61149
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.128288
Minimum3.06 × 10-6
Maximum42.618882
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:48.500865image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum3.06 × 10-6
5-th percentile0.0042324661
Q10.021130774
median0.043495508
Q30.071949208
95-th percentile0.14297723
Maximum42.618882
Range42.618879
Interquartile range (IQR)0.050818434

Descriptive statistics

Standard deviation5.2065731
Coefficient of variation (CV)4.6145781
Kurtosis26.435669
Mean1.128288
Median Absolute Deviation (MAD)0.024673239
Skewness5.1245166
Sum69076.048
Variance27.108404
MonotonicityNot monotonic
2024-05-21T11:58:48.555336image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.036133219 2
 
< 0.1%
0.048452768 2
 
< 0.1%
0.118250648 2
 
< 0.1%
0.051217008 2
 
< 0.1%
0.060587916 2
 
< 0.1%
0.028036774 2
 
< 0.1%
0.097458332 2
 
< 0.1%
0.034131852 2
 
< 0.1%
0.013450934 2
 
< 0.1%
0.004773949 2
 
< 0.1%
Other values (61139) 61202
> 99.9%
ValueCountFrequency (%)
3.06 × 10-61
< 0.1%
3.84 × 10-61
< 0.1%
5.7 × 10-61
< 0.1%
1.27 × 10-51
< 0.1%
1.79 × 10-51
< 0.1%
1.9 × 10-51
< 0.1%
2 × 10-51
< 0.1%
2.07 × 10-51
< 0.1%
2.3 × 10-51
< 0.1%
2.38 × 10-52
< 0.1%
ValueCountFrequency (%)
42.6188823 1
< 0.1%
42.5951275 1
< 0.1%
42.58806301 1
< 0.1%
42.44903972 1
< 0.1%
42.41545401 1
< 0.1%
42.38591859 1
< 0.1%
42.33250232 1
< 0.1%
42.30642544 1
< 0.1%
42.29851714 1
< 0.1%
42.13909932 1
< 0.1%

Recoveries
Real number (ℝ)

Distinct61162
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.76585
Minimum3.56 × 10-5
Maximum4354.4674
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:48.608500image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum3.56 × 10-5
5-th percentile0.33383104
Q11.6267814
median3.3362217
Q35.448862
95-th percentile9.1861333
Maximum4354.4674
Range4354.4674
Interquartile range (IQR)3.8220807

Descriptive statistics

Standard deviation357.22925
Coefficient of variation (CV)5.9771467
Kurtosis58.229151
Mean59.76585
Median Absolute Deviation (MAD)1.8652423
Skewness7.3709138
Sum3658984.9
Variance127612.74
MonotonicityNot monotonic
2024-05-21T11:58:48.661765image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.208043682 2
 
< 0.1%
3.690140334 2
 
< 0.1%
5.151216155 2
 
< 0.1%
9.810005131 2
 
< 0.1%
3.403557223 2
 
< 0.1%
3.121505085 2
 
< 0.1%
1.7711868 2
 
< 0.1%
2.44868672 2
 
< 0.1%
2.811728042 2
 
< 0.1%
0.83272637 2
 
< 0.1%
Other values (61152) 61202
> 99.9%
ValueCountFrequency (%)
3.56 × 10-51
< 0.1%
9.02 × 10-51
< 0.1%
0.000220811 1
< 0.1%
0.000350407 1
< 0.1%
0.000372444 1
< 0.1%
0.00042457 1
< 0.1%
0.000595908 1
< 0.1%
0.000696606 1
< 0.1%
0.000865389 1
< 0.1%
0.000882372 1
< 0.1%
ValueCountFrequency (%)
4354.467419 1
< 0.1%
4339.261318 1
< 0.1%
4330.782063 1
< 0.1%
4325.079801 1
< 0.1%
4313.548899 1
< 0.1%
4299.375307 1
< 0.1%
4262.898182 1
< 0.1%
4254.856665 1
< 0.1%
4241.493689 1
< 0.1%
4220.932555 1
< 0.1%

Collection Recovery Fee
Real number (ℝ)

Distinct61092
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1294059
Minimum3.62 × 10-5
Maximum166.833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:48.713638image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum3.62 × 10-5
5-th percentile0.13053839
Q10.47814998
median0.78234365
Q31.0725631
95-th percentile1.4292624
Maximum166.833
Range166.83296
Interquartile range (IQR)0.59441312

Descriptive statistics

Standard deviation3.5094738
Coefficient of variation (CV)3.1073627
Kurtosis178.25827
Mean1.1294059
Median Absolute Deviation (MAD)0.29678037
Skewness11.175688
Sum69144.489
Variance12.316407
MonotonicityNot monotonic
2024-05-21T11:58:48.771027image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.125373051 3
 
< 0.1%
1.228173443 2
 
< 0.1%
0.839886403 2
 
< 0.1%
1.185926135 2
 
< 0.1%
1.027105363 2
 
< 0.1%
1.254499426 2
 
< 0.1%
1.105250042 2
 
< 0.1%
1.001721823 2
 
< 0.1%
1.42664969 2
 
< 0.1%
1.171319212 2
 
< 0.1%
Other values (61082) 61201
> 99.9%
ValueCountFrequency (%)
3.62 × 10-51
< 0.1%
4.5 × 10-51
< 0.1%
7.34 × 10-51
< 0.1%
0.000144092 1
< 0.1%
0.000184412 1
< 0.1%
0.000236446 1
< 0.1%
0.000359239 1
< 0.1%
0.000390586 1
< 0.1%
0.000400796 1
< 0.1%
0.000485005 1
< 0.1%
ValueCountFrequency (%)
166.833 1
< 0.1%
54.22278838 1
< 0.1%
53.46508418 1
< 0.1%
51.42711665 1
< 0.1%
51.04841185 1
< 0.1%
50.84705334 1
< 0.1%
50.66485507 1
< 0.1%
49.94395918 1
< 0.1%
49.85959833 1
< 0.1%
49.52562248 1
< 0.1%

Collection 12 months Medical
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size478.4 KiB
0
59916 
1
 
1306

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters61222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 59916
97.9%
1 1306
 
2.1%

Length

2024-05-21T11:58:48.825145image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T11:58:48.862935image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 59916
97.9%
1 1306
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 59916
97.9%
1 1306
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 59916
97.9%
1 1306
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 59916
97.9%
1 1306
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 59916
97.9%
1 1306
 
2.1%

Application Type
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size478.4 KiB
INDIVIDUAL
61110 
JOINT
 
112

Length

Max length10
Median length10
Mean length9.990853
Min length5

Characters and Unicode

Total characters611660
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDIVIDUAL
2nd rowINDIVIDUAL
3rd rowINDIVIDUAL
4th rowINDIVIDUAL
5th rowINDIVIDUAL

Common Values

ValueCountFrequency (%)
INDIVIDUAL 61110
99.8%
JOINT 112
 
0.2%

Length

2024-05-21T11:58:48.906276image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T11:58:48.945078image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
individual 61110
99.8%
joint 112
 
0.2%

Most occurring characters

ValueCountFrequency (%)
I 183442
30.0%
D 122220
20.0%
N 61222
 
10.0%
V 61110
 
10.0%
U 61110
 
10.0%
A 61110
 
10.0%
L 61110
 
10.0%
J 112
 
< 0.1%
O 112
 
< 0.1%
T 112
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 611660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 183442
30.0%
D 122220
20.0%
N 61222
 
10.0%
V 61110
 
10.0%
U 61110
 
10.0%
A 61110
 
10.0%
L 61110
 
10.0%
J 112
 
< 0.1%
O 112
 
< 0.1%
T 112
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 611660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 183442
30.0%
D 122220
20.0%
N 61222
 
10.0%
V 61110
 
10.0%
U 61110
 
10.0%
A 61110
 
10.0%
L 61110
 
10.0%
J 112
 
< 0.1%
O 112
 
< 0.1%
T 112
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 611660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 183442
30.0%
D 122220
20.0%
N 61222
 
10.0%
V 61110
 
10.0%
U 61110
 
10.0%
A 61110
 
10.0%
L 61110
 
10.0%
J 112
 
< 0.1%
O 112
 
< 0.1%
T 112
 
< 0.1%

Last week Pay
Real number (ℝ)

Distinct162
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.078665
Minimum0
Maximum161
Zeros113
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:48.991191image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q135
median68
Q3104
95-th percentile147
Maximum161
Range161
Interquartile range (IQR)69

Descriptive statistics

Standard deviation43.324943
Coefficient of variation (CV)0.60953513
Kurtosis-0.98548392
Mean71.078665
Median Absolute Deviation (MAD)35
Skewness0.26453686
Sum4351578
Variance1877.0507
MonotonicityNot monotonic
2024-05-21T11:58:49.053107image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 683
 
1.1%
13 665
 
1.1%
12 658
 
1.1%
11 642
 
1.0%
10 637
 
1.0%
15 628
 
1.0%
16 599
 
1.0%
9 588
 
1.0%
17 567
 
0.9%
8 540
 
0.9%
Other values (152) 55015
89.9%
ValueCountFrequency (%)
0 113
 
0.2%
1 130
 
0.2%
2 187
 
0.3%
3 223
 
0.4%
4 262
0.4%
5 334
0.5%
6 370
0.6%
7 446
0.7%
8 540
0.9%
9 588
1.0%
ValueCountFrequency (%)
161 144
0.2%
160 165
0.3%
159 180
0.3%
158 195
0.3%
157 210
0.3%
156 207
0.3%
155 216
0.4%
154 214
0.3%
153 234
0.4%
152 229
0.4%

Accounts Delinquent
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size478.4 KiB
0
61222 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters61222
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 61222
100.0%

Length

2024-05-21T11:58:49.104114image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T11:58:49.145347image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 61222
100.0%

Most occurring characters

ValueCountFrequency (%)
0 61222
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61222
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61222
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61222
100.0%

Total Collection Amount
Real number (ℝ)

Distinct2073
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.59224
Minimum1
Maximum16421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:49.186107image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q124
median35
Q346
95-th percentile490
Maximum16421
Range16420
Interquartile range (IQR)22

Descriptive statistics

Standard deviation733.6971
Coefficient of variation (CV)5.0742496
Kurtosis209.40093
Mean144.59224
Median Absolute Deviation (MAD)11
Skewness12.947007
Sum8852226
Variance538311.44
MonotonicityNot monotonic
2024-05-21T11:58:49.239708image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 1519
 
2.5%
36 1494
 
2.4%
39 1493
 
2.4%
40 1487
 
2.4%
41 1478
 
2.4%
35 1472
 
2.4%
33 1439
 
2.4%
34 1426
 
2.3%
42 1419
 
2.3%
31 1413
 
2.3%
Other values (2063) 46582
76.1%
ValueCountFrequency (%)
1 287
0.5%
2 301
0.5%
3 319
0.5%
4 360
0.6%
5 371
0.6%
6 424
0.7%
7 448
0.7%
8 486
0.8%
9 528
0.9%
10 565
0.9%
ValueCountFrequency (%)
16421 1
< 0.1%
16385 1
< 0.1%
16086 1
< 0.1%
16013 1
< 0.1%
15956 1
< 0.1%
15916 1
< 0.1%
15895 1
< 0.1%
15663 1
< 0.1%
15460 1
< 0.1%
15459 1
< 0.1%

Total Current Balance
Real number (ℝ)

Distinct55768
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159137.67
Minimum617
Maximum1177412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:49.297508image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum617
5-th percentile18162.05
Q150252.5
median117993
Q3227893
95-th percentile431233.6
Maximum1177412
Range1176795
Interquartile range (IQR)177640.5

Descriptive statistics

Standard deviation138762.05
Coefficient of variation (CV)0.87196232
Kurtosis3.1734917
Mean159137.67
Median Absolute Deviation (MAD)78155.5
Skewness1.5178007
Sum9.7427264 × 109
Variance1.9254907 × 1010
MonotonicityNot monotonic
2024-05-21T11:58:49.360052image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
223061 4
 
< 0.1%
43790 4
 
< 0.1%
34103 4
 
< 0.1%
61768 4
 
< 0.1%
98867 4
 
< 0.1%
44225 4
 
< 0.1%
18957 4
 
< 0.1%
38188 4
 
< 0.1%
24232 4
 
< 0.1%
70908 4
 
< 0.1%
Other values (55758) 61182
99.9%
ValueCountFrequency (%)
617 1
< 0.1%
623 1
< 0.1%
628 1
< 0.1%
630 1
< 0.1%
667 1
< 0.1%
691 1
< 0.1%
707 1
< 0.1%
710 1
< 0.1%
798 1
< 0.1%
803 2
< 0.1%
ValueCountFrequency (%)
1177412 1
< 0.1%
1165601 1
< 0.1%
1157944 1
< 0.1%
1150619 1
< 0.1%
1145991 1
< 0.1%
1140709 1
< 0.1%
1128432 1
< 0.1%
1114351 1
< 0.1%
1091714 1
< 0.1%
1067130 1
< 0.1%
Distinct35674
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23113.298
Minimum1000
Maximum201169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.4 KiB
2024-05-21T11:58:49.416356image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile2716
Q18139.25
median16712.5
Q332125.75
95-th percentile63396.7
Maximum201169
Range200169
Interquartile range (IQR)23986.5

Descriptive statistics

Standard deviation20939.929
Coefficient of variation (CV)0.9059689
Kurtosis6.0090085
Mean23113.298
Median Absolute Deviation (MAD)10392.5
Skewness1.9826469
Sum1.4150423 × 109
Variance4.3848062 × 108
MonotonicityNot monotonic
2024-05-21T11:58:49.762010image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5310 10
 
< 0.1%
12364 9
 
< 0.1%
5642 9
 
< 0.1%
5413 9
 
< 0.1%
4754 9
 
< 0.1%
7026 8
 
< 0.1%
7393 8
 
< 0.1%
10532 8
 
< 0.1%
7058 8
 
< 0.1%
4386 8
 
< 0.1%
Other values (35664) 61136
99.9%
ValueCountFrequency (%)
1000 2
 
< 0.1%
1001 4
< 0.1%
1003 1
 
< 0.1%
1005 3
< 0.1%
1007 1
 
< 0.1%
1008 5
< 0.1%
1009 1
 
< 0.1%
1010 3
< 0.1%
1011 4
< 0.1%
1014 2
 
< 0.1%
ValueCountFrequency (%)
201169 1
< 0.1%
197112 1
< 0.1%
193312 1
< 0.1%
192276 1
< 0.1%
190060 1
< 0.1%
189087 1
< 0.1%
188063 1
< 0.1%
185719 1
< 0.1%
183429 1
< 0.1%
178676 1
< 0.1%

Loan Status
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size478.4 KiB
0
61222 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters61222
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 61222
100.0%

Length

2024-05-21T11:58:49.820073image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T11:58:49.856516image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 61222
100.0%

Most occurring characters

ValueCountFrequency (%)
0 61222
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61222
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61222
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61222
100.0%

Interactions

2024-05-21T11:58:44.805256image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:22.561767image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:23.857016image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:24.916474image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:26.035095image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:27.491148image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:28.624182image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:29.754651image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:30.865755image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:32.063081image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:33.020136image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:33.988999image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:35.165388image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:36.138203image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:37.125607image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:38.206566image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:39.342145image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:40.287992image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:41.406752image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:42.508023image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:43.522235image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:44.864048image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:22.755287image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:23.904535image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:24.964150image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:26.100470image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:27.536826image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:28.678757image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:29.799608image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:30.912938image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:32.108664image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:33.068796image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:34.036651image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:35.210360image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:36.183402image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:37.172728image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:38.252785image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:39.384817image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:40.334580image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:41.456550image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:42.560226image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:43.570625image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:44.918101image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:22.823224image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:23.950239image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:25.009798image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:26.157911image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:27.581664image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:28.728596image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:29.848954image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:30.961896image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:32.152692image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:33.113309image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:34.083443image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:35.254654image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:36.228424image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:37.223648image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:38.299810image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:39.428997image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:40.380774image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:41.503110image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:42.609926image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:43.616724image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:44.976834image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:22.880582image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:23.995728image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:25.058393image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:26.240472image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:27.628474image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:28.778330image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:29.911899image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:31.011414image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:32.197028image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:33.161208image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:34.130510image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:35.299799image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:36.275490image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:37.277787image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:38.348198image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:39.482046image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:40.427276image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:41.552306image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:42.653814image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:43.663940image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:45.038699image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:22.931608image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:24.039656image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:25.101962image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:26.321587image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:27.671701image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:28.826291image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:29.974442image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:31.060031image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:32.244658image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:33.215408image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:34.182046image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:35.350851image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:36.323170image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:37.341868image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:38.394275image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:39.536458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:40.481226image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:41.605162image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:42.700982image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:43.710468image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:45.094255image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:22.978055image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:24.081760image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:25.146403image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:26.385099image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:27.710306image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:28.868281image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:30.032735image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:31.102810image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:32.285663image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:33.254969image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:34.228543image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:35.400532image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:36.364112image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:37.402849image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:38.438716image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:39.577592image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:40.524922image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:41.652409image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:42.748590image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:43.763124image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:45.147884image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:23.028857image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:24.131325image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:25.194155image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:26.455152image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:27.758660image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:28.917109image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:30.086840image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:31.153150image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:32.329735image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:33.305090image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:34.293592image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:35.453466image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:36.420330image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:37.456560image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:38.487383image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:39.621519image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:40.572193image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:41.700517image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:42.805053image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:43.823147image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:45.199027image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:23.079973image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:24.179730image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:25.242729image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:26.516568image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:27.801312image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:28.965917image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:30.138829image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:31.202417image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:32.377713image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:33.350657image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:34.341889image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:35.502314image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:36.471089image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:37.510784image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:38.533569image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:39.667861image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:40.620404image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:41.757354image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:42.852789image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:43.900503image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:45.250843image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:23.130978image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:24.227103image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:25.293614image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:26.703697image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:27.847902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:29.121461image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:30.185417image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:31.249411image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:32.426408image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:33.399619image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:34.393306image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:35.550683image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:36.519182image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:37.562635image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:38.579900image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:39.714999image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:40.667588image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:41.814747image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:42.899206image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:44.199216image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2024-05-21T11:58:35.992849image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:36.970350image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:38.056568image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:39.205515image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:40.149661image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:41.249179image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:42.348741image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:43.371743image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:44.659746image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:45.768128image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:23.751932image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:24.807235image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:25.929891image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:27.372396image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:28.498698image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:29.643111image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:30.764106image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:31.962205image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:32.923463image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:33.890513image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:35.065697image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:36.036438image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:37.017493image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:38.108995image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:39.247901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:40.193163image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:41.297915image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:42.396833image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:43.419785image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:44.704326image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:45.818907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:23.799367image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:24.855463image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:25.978647image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:27.425044image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:28.562087image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:29.697943image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:30.814357image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:32.010956image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:32.968427image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:33.936958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:35.110760image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:36.082904image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:37.068874image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:38.154203image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:39.290776image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:40.238516image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:41.346548image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:42.447832image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:43.470404image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-21T11:58:44.748217image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Missing values

2024-05-21T11:58:45.914520image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-21T11:58:46.139064image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDLoan AmountFunded AmountFunded Amount InvestorTermInterest RateGradeEmployment DurationHome OwnershipVerification StatusDebit to IncomeDelinquency - two yearsInquires - six monthsOpen AccountPublic RecordRevolving BalanceRevolving UtilitiesTotal AccountsInitial List StatusTotal Received InterestTotal Received Late FeeRecoveriesCollection Recovery FeeCollection 12 months MedicalApplication TypeLast week PayAccounts DelinquentTotal Collection AmountTotal Current BalanceTotal Revolving Credit LimitLoan Status
065087372100003223612329.3628605911.135007BMORTGAGE176346.62670Not Verified16.284758101302424674.9325517w2929.6463150.1020552.4982910.7937240INDIVIDUAL4903131130166190
1145015336091194012191.9969205912.237563CRENT39833.92100Source Verified15.4124090012081278.29718613f772.7693850.0361812.3772150.9748210INDIVIDUAL109053182610208850
2196910128276931121603.2245505912.545884FMORTGAGE91506.69105Source Verified28.1376190014018432.07304020w863.32439618.7786604.3162771.0200750INDIVIDUAL6603489801261550
3665143011170695417877.1558505916.731201CMORTGAGE108286.57590Source Verified18.04373010701381967.46795112w288.1731960.0441310.1070200.7499710INDIVIDUAL390409189602140
414354669168901322613539.9266705915.008300CMORTGAGE44234.82545Source Verified17.20988613131154485.25076122w129.23955319.3066461294.8187510.3689530INDIVIDUAL180430126029225790
55050904634631302038635.9316133617.246986BRENT98957.47561Not Verified7.91433332160227751.56447620w464.8181240.0885845.0435750.5816880INDIVIDUAL3204251252274800
632737431308441977315777.5118305910.731432CRENT102391.82430Verified15.083911001101450146.80880437w525.7381090.0835283.1679370.5530760INDIVIDUAL710338842069310680
76315165020744106097645.0148025813.993688AOWN61723.52014Not Verified29.829715001401306723.93662433w1350.2452120.0449650.0984480.0475890INDIVIDUAL87048184909433030
8427966292991123813429.4566105911.178457GMORTGAGE63205.09072Verified26.244710006054915.94738617w4140.1989780.0171060.5302140.2169850INDIVIDUAL1440266812674820
944310341923289627004.097481585.520413CRENT42015.46586Source Verified10.04854910110136135.07334530f2149.6669630.0083382.9122150.8868640INDIVIDUAL903571650148710
IDLoan AmountFunded AmountFunded Amount InvestorTermInterest RateGradeEmployment DurationHome OwnershipVerification StatusDebit to IncomeDelinquency - two yearsInquires - six monthsOpen AccountPublic RecordRevolving BalanceRevolving UtilitiesTotal AccountsInitial List StatusTotal Received InterestTotal Received Late FeeRecoveriesCollection Recovery FeeCollection 12 months MedicalApplication TypeLast week PayAccounts DelinquentTotal Collection AmountTotal Current BalanceTotal Revolving Credit LimitLoan Status
612123802718723118702020949.5257905815.716337CMORTGAGE81967.15226Not Verified34.537577101202738420.71310811f1528.2058510.0719724.5472430.2578830INDIVIDUAL2702624532153600
612131466955431161160008386.746929596.524646AMORTGAGE81220.63670Source Verified34.387740009050551.61331422f1057.8260020.0068611.1090810.9425390INDIVIDUAL90399204397050
6121437933019712258968740.5898415814.729811BMORTGAGE39889.60578Not Verified29.4753350011026325.11376219f2934.5402770.0287370.2556951.0830930INDIVIDUAL36018311173467240
612156096151851271695613917.4852205919.388683CMORTGAGE99748.53668Source Verified33.62229400100129649.16080018f265.4799680.0931461.8051521.0854860INDIVIDUAL3603943981162190
612163902239011703197369972.2026965911.430757EMORTGAGE50548.01172Source Verified32.6376180014077877.36071824f380.9073940.0678631.2332400.7780510INDIVIDUAL290317445397710
612177273094114401767222965.7629005915.025260CRENT76128.78634Verified21.9296980080526012.0806627w2258.0387120.0107220.0610960.3255640INDIVIDUAL151038859647214680
612183518271483231104615637.463010599.972104CRENT65491.12817Source Verified17.69427900120973715.69070314w3100.8031250.0270952.0154941.4033680INDIVIDUAL140372269287140
6121916435904158973292112329.4577505919.650943AMORTGAGE34813.96985Verified10.295774007121951.5000909w2691.9955320.0282125.6730921.6070930INDIVIDUAL137017176857423300
61220530032516567497521353.6846505913.169095DOWN96938.83564Not Verified7.61462400140117268.48188215f3659.3342020.0745081.1574540.2076080INDIVIDUAL73061361339390750
6122165443173153532987514207.4486005916.034631BMORTGAGE105123.15580Verified16.05211200300876281.69232816f1324.2559220.0006711.8564800.3663860INDIVIDUAL54047196960660600